Additive diagnostic value of atherosclerotic plaque characteristics to non-invasive FFR for identification of lesions causing ischemia: results from a prospective international multicenter trial
Bibliographic record
Abstract
Purpose: Non-invasive fractional flow reserve derived from coronary CT angiography (FFRCT) is a novel method for diagnosis of ischemic coronary lesions. Adverse plaque characteristics (APC) by coronary CT angiography (CT)–including positive remodeling (PR), low attenuation plaque (LAP) and spotty calcification (SC)–are associated with myocardial ischemia. To date, whether APCs offer additive value to FFRCT for identifying ischemia-causing lesions remains unknown. Methods: 252 patients at 17 centers in 5 countries were enrolled in the DeFACTO (Diagnostic Accuracy of Fractional Flow Reserve From Anatomic CT Angiography) study. Patients underwent CT, FFRCT, invasive coronary angiography and clinically indicated FFR in 407 lesions. FFRCT, FFR and CT were interpreted by independent core laboratories. Invasive FFR ≤0.80 was diagnostic of lesion-specific ischemia, while CT stenosis ≥70% was considered obstructive. APCs within coronary lesions by CT were defined as: (1) PR, remodeling index >1.10; (2) LAP, any voxel <30 HU; and (3) SC, nodular calcium <3 mm. Discrimination of lesion-specific ischemia was evaluated by areas under the receiver-operating-characteristics curve (AUC). Results: By FFR, ischemia was identified in 151 of 407 lesions (37%), and by CT, obstructive stenoses were identified in 166 (41%) lesions. The presence of any APC was detected in 209 (51%), with PR, LAP and SC observed in 193 (47%), 90 (22%), and 68 (17%) lesions, respectively. The discriminatory power to identify ischemia-causing lesions was 0.73 for CT stenosis alone, 0.84 for FFRCT and 0.87 for FFRCT plus all 3 APCs (p<0.01 compared to FFRCT and CT stenosis alone for both) [Figure]. ROC curves for lesion-specific ischemia Conclusions: APCs improve discrimination of ischemia-causing lesions beyond FFRCT and CT stenosis alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".